Research on coal-rock interface distribution perception based on near-infrared spectra
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摘要: 近红外波段反射光谱能够基于煤岩本质物质属性不同所引起的反射光谱特征差异进行煤岩区分，识别精度高，实时性好，但尚未用于煤岩界面位置分布识别。针对采煤机记忆截割在后续截割循环中对煤岩界面自主判定的实际需求，研究了基于近红外反射光谱技术的煤岩界面分布感知技术。采用气煤、炭质泥页岩切割块样搭建了煤壁煤岩界面台架，设计了光纤准直镜−卤钨聚光光源一体式光谱探头并安装于采煤机机身，在采煤机0，3，7 m/s 3种行走速度和光谱探头3，4，5，6 °/s 4种扫描角速度下，测定了煤岩界面附近煤岩的近红外波段(1 000~2 500 nm)后向反射光谱曲线。对于光谱探头在煤壁上每条扫描轨迹中采集的所有反射光谱，在2 150~2 250 nm差异性特征波段，基于余弦距离模糊C均值聚类(CFCM)进行煤岩反射光谱无监督识别，根据每条扫描轨迹上各位置探测结果，基于高度差权重法和扫描轨迹方程确定煤岩界面点理论探测位置。研究结果表明：在采煤机和光谱探头每种运动状态下，光纤准直镜−卤钨聚光光源一体式光谱探头所采集气煤、炭质泥页岩近红外波段后向反射光谱均具有1 400，1 900，2 200 nm附近明显的差异性吸收谷谱带，随着探测入射角增大，煤岩反射光谱曲线均呈下降趋势；同种采煤机行走速度下，随着光谱探头扫描角速度增大，以及同种光谱探头扫描角速度下，随着采煤机行走速度增大，煤岩反射光谱曲线整体均趋于平缓；基于CFCM、高度差权重法、煤壁扫描轨迹方程可实现采煤机和光谱探头运动状态下煤岩界面点的快速精确探测，其中光谱探头3，4，5 °/s 3种扫描角速度下煤岩界面点探测结果的均方根误差不超过1.5 cm，为近红外反射光谱技术应用于煤岩界面分布的精确高效感知提供了参考。Abstract: Near-infrared reflectance spectra can distinguish coal and rock based on the difference of reflectance spectra characteristics caused by different intrinsic material attributes of coal and rock. This method has high identification accuracy and good real-time performance. But it has not been used for identification of coal-rock interface position distribution. According to the demand for self-determination of the coal-rock interface in the subsequent cutting cycle of shearer memory cutting, the precise distribution sensing technology of coal-rock interface based on near-infrared reflectance spectra technology is studied. A coal-rock interface platform is built by using gas coal and carbonaceous shale cutting block samples. A spectrum detector integrated with optical fiber collimator and tungsten halogen light source is designed and installed on the shearer's body. The near-infrared (1 000-2 500 nm) backward reflectance spectra curves of coal and rock near the coal-rock interface are measured at three walking velocities of the shearer (0, 3, 7 m/s) and four scanning angular velocities of the spectrum detector (3, 4, 5, 6 °/s). For all the reflectance spectra collected by the spectrum detector in each scanning track on the coal wall, the unsupervised identification of coal-rock reflection spectra is carried out based on cosine distance fuzzy C-means clustering (CFCM) in the differential characteristic wave bands of 2 150-2 250 nm. According to the detection results of each position on each scanning trajectory, the theoretical detection position of the coal-rock interface point is determined based on the height difference weighting method and scanning trajectory equation. The research result shows that under each movement state of the shearer and the spectrum detector, the near-infrared reflectance spectra in the backward direction of gas coal and carbonaceous shale collected by the integrated optical fiber collimator tungsten halogen light source spectrum detector have obvious differential absorption valley bands around 1400, 1900, 2200 nm. The reflectance spectra curves of coal and rock all show a downward trend with the increase of the detection incident angle. With the increase of the scanning angular velocities of the spectrum detectors under the same walking velocity of the shearer, and with the increase of the walking velocity of the shearer under the same scanning angular velocity of the spectrum detector, the reflectance spectra curves of coal and rock tend to be flat as a whole. Based on CFCM, height difference weighting method and coal wall scanning trajectory equation, rapid and precise detection of coal-rock interface points under the movement of the shearer and spectrum detector can be realized. Among them, the root mean square error of the detection results of coal-rock interface points under three scanning angular velocities of spectrum detectors 3, 4, 5 °/s is not more than 1.5 cm. The research provides a reference for the application of near-infrared reflectance spectra technology to the precise and efficient perception of coal-rock interface distribution.
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图 2 煤岩反射光谱采集实验平台
Figure 2. Experimental platform of coal-rock reflectance spectra detection
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图 3 光谱探头探测模拟煤壁横向视图
Figure 3. Transverse view of spectrum detector detecting simulated coal wall
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图 5 采煤机静止（v=0)时光谱探头各扫描角速度下煤岩代表性近红外后向反射光谱
Figure 5. Representative near-infrared reflectance spectra in the backward direction of coal and rock under each scanning angular velocity of spectrum detector when the shearer is stationary（v=0)
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图 6 采煤机静止（v=0)时光谱探头各扫描角速度下煤岩界面探测结果
Figure 6. Detection results of coal-rock interface under each scanning angular velocity of spectrum detector when the shearer is stationary（v=0)
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图 7 采煤机以3 m/s速度行走时光谱探头各扫描角速度下煤岩代表性近红外后向反射光谱
Figure 7. Representative near-infrared reflectance spectra in the backward direction of coal and rock under each scanning angular velocity of spectrum detector when the shearer moves at 3 m/s
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图 8 采煤机以7 m/s速度行走时光谱探头各扫描角速度下煤岩代表性近红外后向反射光谱
Figure 8. Representative near-infrared reflectance spectra in the backward direction of coal and rock under each scanning angular velocity of spectrum detector when the shearer moves at 7 m/s
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图 9 采煤机以3 m/s速度行走时光谱探头各扫描角速度下煤岩界面探测结果
Figure 9. Detection results of coal-rock interface under each scanning angular velocity of spectrum detector when the shearer moves at 3 m/s
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图 10 采煤机以7 m/s速度行走时光谱探头各扫描角速度下煤岩界面探测结果
Figure 10. Detection results of coal-rock interface under each scanning angular velocity of spectrum detector when the shearer moves at 7 m/s
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[1] 葛世荣. 采煤机技术发展历程(六)−煤岩界面探测[J]. 中国煤炭,2020,46(11):10-24. DOI: 10.3969/j.issn.1006-530X.2020.11.002 GE Shirong. The development history of coal shearer technology(part six):coal-rock interface detection[J]. China Coal,2020,46(11):10-24. DOI: 10.3969/j.issn.1006-530X.2020.11.002
[2] 张强,张润鑫,刘峻铭,等. 煤矿智能化开采煤岩识别技术综述[J]. 煤炭科学技术,2022,50(2):1-26. DOI: 10.13199/j.cnki.cst.2021-1333 ZHANG Qiang,ZHANG Runxin,LIU Junming,et al. Review on coal and rock identification technology for intelligent mining in coal mines[J]. Coal Science and Technology,2022,50(2):1-26. DOI: 10.13199/j.cnki.cst.2021-1333
[3] 庞义辉. 综采工作面智能化发展现状与展望[J]. 智能矿山,2020,1(1):21-29. PANG Yihui. Intelligent development situation and prospect of fully mechanized mining face[J]. Journal of Intelligent Mine,2020,1(1):21-29.
[4] SONG Zeyang,KUENZER C. Spectral reflectance (400-2 500 nm) properties of coals,adjacent sediments,metamorphic and pyrometamorphic rocks in coal-fire areas:a case study of Wuda coalfield and its surrounding areas,northern China[J]. International Journal of Coal Geology,2017,171:142-152. DOI: 10.1016/j.coal.2017.01.008
[5] MAO Yachun,MA Baodong,LIU Shanjun,et al. Study and validation of a remote sensing model for coal extraction based on reflectance spectrum features[J]. Canadian Journal of Remote Sensing,2014,40(5):327-335. DOI: 10.1080/07038992.2014.979486
[6] MILTON E J,SCHAEPMAN M E,ANDERSON K,et al. Progress in field spectroscopy[J]. Remote Sensing of Environment,2009,113:S92-S109. DOI: 10.1016/j.rse.2007.08.001
[7] 杨恩,王世博,葛世荣. 典型块状煤的可见−近红外光谱特征研究[J]. 光谱学与光谱分析,2019,39(6):1717-1723. YANG En,WANG Shibo,GE Shirong. Study on the visible and near-infrared spectra of typical types of lump coal[J]. Spectroscopy and Spectral Analysis,2019,39(6):1717-1723.
[8] 杨恩,王世博,葛世荣. 典型煤系岩石的可见−近红外光谱特征研究[J]. 工矿自动化,2019,45(3):45-51,89. DOI: 10.13272/j.issn.1671-251x.2018090047 YANG En,WANG Shibo,GE Shirong. Research on visible-near infrared spectrum features of typical coal measures rocks[J]. Industry and Mine Automation,2019,45(3):45-51,89. DOI: 10.13272/j.issn.1671-251x.2018090047
[9] YANG En,GE Shirong,WANG Shibo. Characterization and identification of coal and carbonaceous shale using visible and near-infrared reflectance spectroscopy[J]. Journal of Spectroscopy,2018(2):1-13.
[10] 杨恩,王世博,葛世荣,等. 煤岩界面的高光谱识别原理[J]. 煤炭学报,2018,43(增刊2):646-653. DOI: 10.13225/j.cnki.jccs.2018.0098 YANG En,WANG Shibo,GE Shirong,et al. Study on the principle of hyperspectral recognition of coal-rock interface[J]. Journal of China Coal Society,2018,43(S2):646-653. DOI: 10.13225/j.cnki.jccs.2018.0098
[11] 杨恩,王世博,葛世荣,等. 基于反射光谱的煤岩感知实验研究[J]. 煤炭学报,2019,44(12):3912-3920. YANG En,WANG Shibo,GE Shirong,et al. Experimental study on coal-rock perception based on reflectance spectroscopy[J]. Journal of China Coal Society,2019,44(12):3912-3920.
[12] GOETZ A F H,CURTISS B,SHILEY D A. Rapid gangue mineral concentration measurement over conveyors by NIR reflectance spectroscopy[J]. Minerals Engineering,2009,22(5):490-499. DOI: 10.1016/j.mineng.2008.12.013
[13] FANG Qian,HONG Hanlie,ZHAO Lulu,et al. Visible and near-infrared reflectance spectroscopy for investigating soil mineralogy:a review[J]. Journal of Spectroscopy,2018,2018:1-14.
[14] PASQUINI C. Near infrared spectroscopy:a mature analytical technique with new perspectives:a review[J]. Analytica Chimica Acta,2018,1026:8-36. DOI: 10.1016/j.aca.2018.04.004
[15] SCHOPFER J,DANGEL S,KNEUBÜHLER M,et al. The improved dual-view field goniometer system FIGOS[J]. Sensors,2008,8:5120-5140. DOI: 10.3390/s8085120
[16] CLOUTIS E A,PIETRASZ V B,KIDDELL C,et al. Spectral reflectance "deconstruction" of the Murchison CM2 carbonaceous chondrite and implications for spectroscopic investigations of dark asteroids[J]. Icarus,2018,305:203-224. DOI: 10.1016/j.icarus.2018.01.015
[17] 赵子傑,赵云升. 不同粒径沙地表面双向反射特性研究[J]. 物理学报,2014,63(18):435-441. ZHAO Zijie,ZHAO Yunsheng. Bidirectional reflectance of sandy land surface with different particle sizes[J]. Acta Physica Sinica,2014,63(18):435-441.
[18] 路鹏,陈圣波,崔腾飞,等. 月球表面矿物二向性反射特性实验研究[J]. 岩石学报,2016,32(1):107-112. LU Peng,CHEN Shengbo,CUI Tengfei,et al. Experimental study on bidirectional reflectance characteristics of minerals on lunar surface[J]. Acta Petrologica Sinica,2016,32(1):107-112.
[19] 杨恩,王世博. 典型煤的近红外反射光谱特征的方向性研究[J]. 光谱学与光谱分析,2022,42(3):847-858. YANG En,WANG Shibo. Study on directional near-infrared reflectance spectra of typical types of coal[J]. Spectroscopy and Spectral Analysis,2022,42(3):847-858.
[20] 梁吉智,韩培毅,郭天骏,等. 综采工作面智能化装备关键技术与应用[J]. 煤炭科学技术,2021,49(增刊1):59-62. LIANG Jizhi,HAN Peiyi,GUO Tianjun,et al. Key technology and application of intelligent equipment in fully-mechanized coal mining face[J]. Coal Science and Technology,2021,49(S1):59-62.
[21] 杨恩,王世博,王赛亚,等. 典型煤岩反射光谱无监督感知方法研究[J]. 工矿自动化,2020,46(1):50-58. DOI: 10.13272/j.issn.1671-251x.2019050078 YANG En,WANG Shibo,WANG Saiya,et al. Research on unsupervised sensing methods of typical coal and rock based on reflectance spectroscopy[J]. Industry and Mine Automation,2020,46(1):50-58. DOI: 10.13272/j.issn.1671-251x.2019050078
[22] WU Fang,WEI Liangshu,HUANG Junjie,et al. Optimized fuzzy C-means clustering algorithm for the interpretation of the near-infrared spectra of rocks[J]. Spectroscopy Letters,2017,50(5):270-274. DOI: 10.1080/00387010.2017.1317271
[23] 李玉,甄畅,石雪,等. 基于熵加权K−means全局信息聚类的高光谱图像分类[J]. 中国图象图形学报,2019,24(4):630-638. DOI: 10.11834/jig.180502 LI Yu,ZHEN Chang,SHI Xue,et al. Hyperspectral image classification algorithm based on entropy weighted K-means with global information[J]. Journal of Image and Graphics,2019,24(4):630-638. DOI: 10.11834/jig.180502
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